Machine Learning Scientist I​/II, Medicinal Chemistry & Lead Optimization

Machine Learning Scientist I/II, Medicinal Chemistry & Lead Optimization

Cambridge, MA USA

Join our Drug Discovery group to build and deploy ligand-based AI that turns noisy, real-world assay data into decisive design guidance for hit-to-lead and lead optimization. You’ll create QSAR models, retrosynthesis-aware generative design tools, and active-learning loops that partner with medicinal chemists to deliver better compounds, faster. This role complements our structure-based docking team by focusing on assay-driven, synthesis-constrained optimization—even when structures are uncertain or unavailable—ultimately accelerating DMTA cycles and improving candidate quality.

• Ligand-based QSAR modeling:
  Develop multi-task and transfer-learned models for potency, selectivity, and develop ability (e.g., solubility, permeability, clearance, CYP/hERG, safety liabilities) using graph/message-passing, and conformer-aware features. Handle activity cliffs, applicability domain, and calibration for robust decision-making.
• Assay-driven hit triage and prioritization:
  Build models that learn from HTS, DEL, and follow-up assays; robust curve-fitting (4PL/5PL), plate/batch effect correction, dose–response QC, and time-split/scaffold-split evaluations to ensure prospective reliability.
• Closed-loop DMTA and MPO:
  Create active learning and Bayesian optimization strategies to propose the next best analogs under multi-parameter objectives (potency, selectivity, exposure, safety, IP). Incorporate uncertainty, diversity, and experimental cost to maximize information gain per cycle.
• Synthesis-aware design and retrosynthesis:
  Integrate template-based and template-free retrosynthesis with reaction prediction, condition and yield modeling, building-block availability, and cost/time/risk scoring. Make design suggestions that are directly makeable and prioritize routes compatible with internal/partner capabilities.
• Generative and enumerative libraries:
  Build BRICS/RECAP/fragment-linking enumerations and property-conditioned generative models (diffusion/RL/flow) that respect synthetic constraints and matched molecular pair (MMP) rules for local SAR exploration and scaffold hopping.
• SAR mining and explainability:
  Automate MMP analysis, local SAR maps, and substructure attributions to surface chemist-actionable insights; link assay deltas to specific modifications and highlight potential bioisosteres and de-risking moves.
• Data foundations:
  Establish cheminformatics pipelines for standardization (tautomer/salt/charge), deduplication, structure normalization, and assay/ELN/LIMS ingestion; define ontologies and metadata for traceability and reproducibility.
• Rigorous evaluation and deployment:
  Design leakage-safe splits (scaffold, temporal, series-aware), conformal prediction for calibrated decisions, and prospective tests. Ship APIs and tools that integrate with medchem workflows, procurement, and automated synthesis.
• Cross-functional partnership:
  Work closely with medicinal chemists, DMPK, biology, and automation to translate TPPs into modeling objectives and to operationalize model recommendations in real make–test cycles. Collaborate with structure-based colleagues to fuse physics- and assay-derived signals where beneficial.

• Strong proficiency in Python and modern ML (PyTorch/JAX/TF, scikit-learn, XGBoost/Cat Boost), with experience training at scale and deploying end-to-end pipelines.
• Deep experience in ligand-based modeling (QSAR/QSPR, multi-task learning, uncertainty and applicability domain, calibration) and ADMET prediction for medicinal chemistry.
• Solid grasp of medicinal chemistry principles: SAR development, bioisosteres, property tuning (pKa/logD/PSA), selectivity design, and liability mitigation (CYP, hERG, reactivity, permeability, solubility).
• Cheminformatics and data tooling: RDKit, Chemprop/Deep Chem, conformer generation, fingerprints/descriptors, ELN/LIMS integration, and assay data processing/curve-fitting.
• Retrosynthesis and synthesis planning:
  Familiarity with template-based/template-free methods, route scoring, reaction/yield/condition prediction, building block catalogs,…